Evidence map›Paper›PMID 41606229›Full record

ArticleInterdisciplinary sciences, computational life sciences2026

Inferring Phenotypes of Single Cells Based on the Expression Profiles of Phenotype-Associated Marker Genes in Bulks and Single Cells.

Yin He, Rongzhuo Long, Xiaosheng Wang

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Article in Interdisciplinary sciences, computational life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yin HeBiomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Rongzhuo LongBiomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Xiaosheng WangCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, 77030, USA. xiaosheng.wang@uth.tmc.edu.ORCID http://orcid.org/0000-0002-7199-7093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell transcriptomes are not sufficient in describing cell phenotypes. Here we propose an algorithm for single cells' phenotype prediction (ScPP) based on the expression profiles of phenotype-associated marker genes in bulks and single cells. ScPP first analyzes bulk data to identify phenotype-associated marker genes. Next, ScPP evaluates the enrichment scores of the marker genes in single cells using the AUCell algorithm. The single cells with the phenotype are defined as the intersection of the single cells with top α ranks according to the phenotype-associated AUC values and the single cells with bottom α ranks according to the opposite phenotype-associated AUC values. Finally, all single cells are determined as phenotype

Indexed as

BiomarkersGene Expression ProfilingSingle-Cell AnalysisTranscriptomeAlgorithmsAnimalsGenetic MarkersHumansPhenotypeSingle-Cell Gene Expression AnalysisBiomarkersGenetic MarkersPhenotype-associated marker geneSingle-cell phenotype predictionTranscriptome

Identifiers

PMID41606229

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.